HAL : hal-00638787, version 1

DOI : 10.1016/j.sigpro.2011.08.014

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Signal Processing 92, 2 (2012) 601-606

On the Use of First-order Autoregressive Modeling for Rayleigh Flat Fading Channel Estimation with Kalman Filter

Soukayna Ghandour - Haidar 1, Laurent Ros 1, Jean-Marc Brossier 1

(02/2012)

This letter deals with the estimation of a flat fading Rayleigh channel with Jakes's spectrum. The channel is approximated by a first-order autoregressive (AR(1)) model and tracked by a Kalman Filter (KF). The common method used in the literature to estimate the parameter of the AR(1) model is based on a Correlation Matching (CM) criterion. However, for slow fading variations, another criterion based on the Minimization of the Asymptotic Variance (MAV) of the KF is more appropriate, as already observed in few works [1]. This letter gives analytic justification by providing approximated closed-form expressions of the estimation variance for the CM and MAV criteria, and of the optimal AR(1) parameter.

1 :	Grenoble Images Parole Signal Automatique (GIPSA-lab)
	CNRS : UMR5216 – Université Joseph Fourier - Grenoble I – Université Pierre-Mendès-France - Grenoble II – Université Stendhal - Grenoble III – Institut Polytechnique de Grenoble - Grenoble Institute of Technology

Domaine

:

Sciences de l'ingénieur/Traitement du signal et de l'image Informatique/Traitement du signal et de l'image

Mots Clés : Channel estimation – Autoregressive model – Kalman Filter – Jakes's spectrum – Rayleigh channel – Flat fading – Bayesian Cramér-Rao Bounds BCRB.

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Contributeur : Laurent Ros <>

Soumis le : Lundi 7 Novembre 2011, 13:01:36

Dernière modification le : Lundi 7 Novembre 2011, 13:32:00